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Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model

Smith, Ryan N., Chao, Yi, Li, Peggy P., Caron, David A., Jones, Burton H., & Sukhatme, Gaurav S. (2010) Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model. International Journal of Robotics Research, 29(12), pp. 1475-1497.

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Abstract

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend prelim- inary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean.

Impact and interest:

48 citations in Scopus
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22 citations in Web of Science®

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ID Code: 40119
Item Type: Journal Article
Additional URLs:
Keywords: Autonomous Underwater Vehicle, Ocean modeling, Algal bloom, Path Planning, Slocum glider
DOI: 10.1177/0278364910377243
ISSN: 0278-3649
Subjects: Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > OCEANOGRAPHY (040500) > Biological Oceanography (040501)
Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > OCEANOGRAPHY (040500) > Physical Oceanography (040503)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MARITIME ENGINEERING (091100) > Ocean Engineering (091103)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2010 the authors.
Deposited On: 28 Mar 2011 12:04
Last Modified: 01 Mar 2012 13:36

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